【AI之路】使用huggingface_hub优雅解决huggingface大模型下载问题

文章目录

前言
一、Hugging face是什么？
二、准备工作
三、下载整个仓库或单个大模型文件
- 1. 下载整个仓库
- 2. 下载单个大模型文件
总结
附录

前言

Hugging face 资源很不错，可是国内下载速度很慢，动则GB的大模型，下载很容易超时，经常下载不成功。很是影响玩AI的信心。（有人说用迅雷啊，试试就知道有无奈。）

经过多次测试，终于搞定了下载，即使超时也可以继续下载。真正实现下载无忧！究竟如何实现？且看本文分解。

再有模型下不了的，请移步：
【AI之路】使用huggingface_hub通过huggingface镜像站下载大模型（附代码，实现大模型*）

一、Hugging face是什么？

官网网址：/

二、准备工作

安装pip install huggingface_hub安装huggingface_hub包

C:\Users\Administrator>pip install huggingface_hub
Requirement already satisfied: huggingface_hub in d:\programdata\anaconda3\lib\site-packages (0.13.2)
Requirement already satisfied: pyyaml>=5.1 in d:\programdata\anaconda3\lib\site-packages (from huggingface_hub) (6.0)
Requirement already satisfied: typing-extensions>=3.7.4.3 in d:\programdata\anaconda3\lib\site-packages (from huggingface_hub) (4.4.0)
Requirement already satisfied: packaging>=20.9 in d:\programdata\anaconda3\lib\site-packages (from huggingface_hub) (22.0)
Requirement already satisfied: requests in d:\programdata\anaconda3\lib\site-packages (from huggingface_hub) (2.28.2)
Requirement already satisfied: tqdm>=4.42.1 in d:\programdata\anaconda3\lib\site-packages (from huggingface_hub) (4.64.1)
Requirement already satisfied: filelock in d:\programdata\anaconda3\lib\site-packages (from huggingface_hub) (3.12.0)
Requirement already satisfied: colorama in d:\programdata\anaconda3\lib\site-packages (from tqdm>=4.42.1->huggingface_hub) (0.4.6)
Requirement already satisfied: charset-normalizer<4,>=2 in d:\programdata\anaconda3\lib\site-packages (from requests->huggingface_hub) (2.0.4)
Requirement already satisfied: idna<4,>=2.5 in d:\programdata\anaconda3\lib\site-packages (from requests->huggingface_hub) (3.4)
Requirement already satisfied: certifi>=2017.4.17 in d:\programdata\anaconda3\lib\site-packages (from requests->huggingface_hub) (2022.12.7)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in d:\programdata\anaconda3\lib\site-packages (from requests->huggingface_hub) (1.26.14)
C:\Users\Administrator>

三、下载整个仓库或单个大模型文件

找到自己需要下载的仓库，红色框即是repo_id ，选择蓝色框即可查看仓库内文件。
在这里插入图片描述

1. 下载整个仓库

使用snapshot_download下载整个仓库快照，如下几个参数注意下：

allow_patterns选择需要下载的文件类型，通过ignore_patterns设置需要忽略的文件类型。
resume_download=True，表示允许断点续传，整个很有必要。
etag_timeout=100，超时阈值，默认10秒，这里自己根据情况修改。
更多参数详情可访问：/docs/huggingface_hub/v0.16.3/guides/download

import time
from huggingface_hub import snapshot_download
repo_id = "LinkSoul/Chinese-Llama-2-7b"
local_dir = 'd:/ai/models1'
cache_dir = local_dir + "/cache"
while True:
    try:
        snapshot_download(cache_dir=cache_dir,
        local_dir=local_dir,
        repo_id=repo_id,
        local_dir_use_symlinks=False,
        resume_download=True,
        allow_patterns=["*.model", "*.json", "*.bin",
        "*.py", "*.md", "*.txt"],
        ignore_patterns=["*.safetensors", "*.msgpack",
        "*.h5", "*.ot",],
        )
    except Exception as e :
        print(e)
        # (5)
    else:
        print('下载完成')
        break

在这里插入图片描述

2. 下载单个大模型文件

个别情况下，我们只需要下载大模型文件，不需要下载整个项目仓库，使用hf_hub_download进行下载即可。参数设置同上解释。

import time
from huggingface_hub import hf_hub_download
repo_id = "BlinkDL/rwkv-4-pile-7b" # 仓库ID
local_dir = 'd:/ai/models2'
cache_dir = local_dir + "/cache"
filename= ""
while True:   
    try:
        hf_hub_download(cache_dir=cache_dir,
        local_dir=local_dir,
        repo_id=repo_id,
        filename=filename,
        local_dir_use_symlinks=False,
        resume_download=True,
        etag_timeout=100
        )
    except Exception as e :
        print(e)
        # (5)
    else:
        print('下载完成')
        break